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However, current defense mechanisms against adversarial attacks often depend on supplementary machine learning frameworks, such as anomaly detection models or Byzantine\u2010robust aggregators. These frameworks add significant computational overhead, straining edge devices such as smart meters and IoT systems with limited processing power. To solve this issue, we propose a new defense\u2010free framework called federated random layer aggregation (FedRLA). By aggregating only one randomly chosen neural network layer per communication round, FedRLA limits adversarial influence to isolated layers. This reduces attack surfaces by 66% compared to full\u2010model aggregation (FedAvg). Using 8\u2010bit quantization, FedRLA cuts data transmission by 92.97% without accuracy loss (MAE: 0.08\u2009kWh vs. FedAvg\u2019s 0.076\u2009kWh). Under four model poisoning attacks, it reduces forecasting errors by 19%\u201335% compared to FedAvg. FedRLA also uses 24% less CPU and 13% less memory than frameworks such as FedProx, while training 58% faster. It combines communication efficiency (0.195\u2009MB\/round), adversarial robustness (MAE \u2264 0.11\u2009kWh under\n                    <jats:italic>\u03f5<\/jats:italic>\n                    \u2009=\u20090.2 DP), and low resource consumption, offering a scalable solution for secure FL in resource\u2010constrained energy networks.\n                  <\/jats:p>","DOI":"10.1155\/int\/8810907","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:02:28Z","timestamp":1776337348000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Trustworthy Distributed Load Forecasting in Resource\u2010Limited Smart Grids and Buildings via Random Layer Aggregation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0192-7353","authenticated-orcid":false,"given":"Habib Ullah","family":"Manzoor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Attia","family":"Shabbir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0855-9864","authenticated-orcid":false,"given":"Rao Naveed Bin","family":"Rais","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-9728","authenticated-orcid":false,"given":"Sajjad","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7497-9336","authenticated-orcid":false,"given":"Ahmed","family":"Zoha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_2_15_1_2","unstructured":"Energy 2020 Un Environment Programme"},{"key":"e_1_2_15_2_2","unstructured":"Energy and Climate Change 2017 European Environment Agency"},{"key":"e_1_2_15_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2020.100015"},{"key":"e_1_2_15_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2024.3406946"},{"key":"e_1_2_15_5_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/B978-0-12-812959-3.00003-4","volume-title":"Advances in Renewable Energies and Power Technologies","author":"Notton G.","year":"2018"},{"key":"e_1_2_15_6_2","article-title":"Western Wind and Solar Integration Study","author":"Energy G.","year":"2010","journal-title":"Citeseer, Tech. 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